Technical Losses Reduction in Underground Reticulated Distribution Systems using Artificial Neural Networks and Smart Grid Features

Detalhes bibliográficos
Autor(a) principal: Cambraia,Mario Sergio
Data de Publicação: 2018
Outros Autores: Brandão Júnior,Augusto Ferreira, Rosa,Luiz Henrique Leite
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Brazilian Archives of Biology and Technology
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132018000200205
Resumo: ABSTRACT This work presents the methodology, development and testing of an autonomous system, based on Artificial Neural Networks (ANN), for the reduction of technical losses in reticulated underground systems through the optimal control of the capacitor banks (CBs) present in the grid. The proposed methodology includes Smart Grid features, including practical solutions for current transformers positioning in underground networks, collecting field measurements for the Distribution Operation Centre (DOC) and real-time control of field equipment (capacitors banks). The steps of the proposed methodology and the main aspects of the development of the system are also described, as well as the tests performed to prove the results and validate the system.
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spelling Technical Losses Reduction in Underground Reticulated Distribution Systems using Artificial Neural Networks and Smart Grid Featuressmart gridsartificial neural networksunderground reticulated networkstechnical lossesABSTRACT This work presents the methodology, development and testing of an autonomous system, based on Artificial Neural Networks (ANN), for the reduction of technical losses in reticulated underground systems through the optimal control of the capacitor banks (CBs) present in the grid. The proposed methodology includes Smart Grid features, including practical solutions for current transformers positioning in underground networks, collecting field measurements for the Distribution Operation Centre (DOC) and real-time control of field equipment (capacitors banks). The steps of the proposed methodology and the main aspects of the development of the system are also described, as well as the tests performed to prove the results and validate the system.Instituto de Tecnologia do Paraná - Tecpar2018-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132018000200205Brazilian Archives of Biology and Technology v.61 n.spe 2018reponame:Brazilian Archives of Biology and Technologyinstname:Instituto de Tecnologia do Paraná (Tecpar)instacron:TECPAR10.1590/1678-4324-smart-2018000180info:eu-repo/semantics/openAccessCambraia,Mario SergioBrandão Júnior,Augusto FerreiraRosa,Luiz Henrique Leiteeng2018-10-18T00:00:00Zoai:scielo:S1516-89132018000200205Revistahttps://www.scielo.br/j/babt/https://old.scielo.br/oai/scielo-oai.phpbabt@tecpar.br||babt@tecpar.br1678-43241516-8913opendoar:2018-10-18T00:00Brazilian Archives of Biology and Technology - Instituto de Tecnologia do Paraná (Tecpar)false
dc.title.none.fl_str_mv Technical Losses Reduction in Underground Reticulated Distribution Systems using Artificial Neural Networks and Smart Grid Features
title Technical Losses Reduction in Underground Reticulated Distribution Systems using Artificial Neural Networks and Smart Grid Features
spellingShingle Technical Losses Reduction in Underground Reticulated Distribution Systems using Artificial Neural Networks and Smart Grid Features
Cambraia,Mario Sergio
smart grids
artificial neural networks
underground reticulated networks
technical losses
title_short Technical Losses Reduction in Underground Reticulated Distribution Systems using Artificial Neural Networks and Smart Grid Features
title_full Technical Losses Reduction in Underground Reticulated Distribution Systems using Artificial Neural Networks and Smart Grid Features
title_fullStr Technical Losses Reduction in Underground Reticulated Distribution Systems using Artificial Neural Networks and Smart Grid Features
title_full_unstemmed Technical Losses Reduction in Underground Reticulated Distribution Systems using Artificial Neural Networks and Smart Grid Features
title_sort Technical Losses Reduction in Underground Reticulated Distribution Systems using Artificial Neural Networks and Smart Grid Features
author Cambraia,Mario Sergio
author_facet Cambraia,Mario Sergio
Brandão Júnior,Augusto Ferreira
Rosa,Luiz Henrique Leite
author_role author
author2 Brandão Júnior,Augusto Ferreira
Rosa,Luiz Henrique Leite
author2_role author
author
dc.contributor.author.fl_str_mv Cambraia,Mario Sergio
Brandão Júnior,Augusto Ferreira
Rosa,Luiz Henrique Leite
dc.subject.por.fl_str_mv smart grids
artificial neural networks
underground reticulated networks
technical losses
topic smart grids
artificial neural networks
underground reticulated networks
technical losses
description ABSTRACT This work presents the methodology, development and testing of an autonomous system, based on Artificial Neural Networks (ANN), for the reduction of technical losses in reticulated underground systems through the optimal control of the capacitor banks (CBs) present in the grid. The proposed methodology includes Smart Grid features, including practical solutions for current transformers positioning in underground networks, collecting field measurements for the Distribution Operation Centre (DOC) and real-time control of field equipment (capacitors banks). The steps of the proposed methodology and the main aspects of the development of the system are also described, as well as the tests performed to prove the results and validate the system.
publishDate 2018
dc.date.none.fl_str_mv 2018-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132018000200205
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132018000200205
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/1678-4324-smart-2018000180
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Instituto de Tecnologia do Paraná - Tecpar
publisher.none.fl_str_mv Instituto de Tecnologia do Paraná - Tecpar
dc.source.none.fl_str_mv Brazilian Archives of Biology and Technology v.61 n.spe 2018
reponame:Brazilian Archives of Biology and Technology
instname:Instituto de Tecnologia do Paraná (Tecpar)
instacron:TECPAR
instname_str Instituto de Tecnologia do Paraná (Tecpar)
instacron_str TECPAR
institution TECPAR
reponame_str Brazilian Archives of Biology and Technology
collection Brazilian Archives of Biology and Technology
repository.name.fl_str_mv Brazilian Archives of Biology and Technology - Instituto de Tecnologia do Paraná (Tecpar)
repository.mail.fl_str_mv babt@tecpar.br||babt@tecpar.br
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